# Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model's Empathy

arXiv: https://arxiv.org/abs/2510.10328 · HTML: https://mlsanity.com/paper/2510.10328/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 98 references that are in the map.
- First posted on arXiv 11 Oct 2025.

## Builds on (most cited first, 30 of 34)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://mlsanity.com/paper/2305.18189/) (2023)
- [Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs](https://mlsanity.com/paper/2311.04892/) (2023)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://mlsanity.com/paper/2010.01454/) (2020)
- [Evaluating Large Language Model Biases in Persona-Steered Generation](https://mlsanity.com/paper/2405.20253/) (2024)
- [Aligning LLMs with Individual Preferences via Interaction](https://mlsanity.com/paper/2410.03642/) (2024)
- [The opportunities and risks of large language models in mental health](https://mlsanity.com/paper/2403.14814/) (2024)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [Knowledge Bridging for Empathetic Dialogue Generation](https://mlsanity.com/paper/2009.09708/) (2020)
- [Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution](https://mlsanity.com/paper/2403.03121/) (2024)
- [Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas](https://mlsanity.com/paper/2408.06929/) (2024)
- [Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances](https://mlsanity.com/paper/2407.21315/) (2024)
- [Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages](https://mlsanity.com/paper/1812.10411/) (2018)
- [EmoNets: Multimodal deep learning approaches for emotion recognition in video](https://mlsanity.com/paper/1503.01800/) (2015)
- [The Colorful Future of LLMs: Evaluating and Improving LLMs as Emotional Supporters for Queer Youth](https://mlsanity.com/paper/2402.11886/) (2024)
- [Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification](https://mlsanity.com/paper/2402.18502/) (2024)
- [Lived Experience Not Found: LLMs Struggle to Align with Experts on Addressing Adverse Drug Reactions from Psychiatric Medication Use](https://mlsanity.com/paper/2410.19155/) (2024)
- [Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction](https://mlsanity.com/paper/2406.12707/) (2024)
- [Can LLM-Simulated Practice and Feedback Upskill Human Counselors? A Randomized Study with 90+ Novice Counselors](https://mlsanity.com/paper/2505.02428/) (2025)
- [APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation](https://mlsanity.com/paper/2407.21048/) (2024)
- [Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning](https://mlsanity.com/paper/2408.11599/) (2024)
- [Cross-lingual and Multilingual Speech Emotion Recognition on English and French](https://mlsanity.com/paper/1803.00357/) (2018)
- [Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels](https://mlsanity.com/paper/2101.03477/) (2021)
- [The Hidden Space of Safety: Understanding Preference-Tuned LLMs in Multilingual context](https://mlsanity.com/paper/2504.02708/) (2025)
- [Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children](https://mlsanity.com/paper/2309.00138/) (2023)
- [CTSM: Combining Trait and State Emotions for Empathetic Response Model](https://mlsanity.com/paper/2403.15516/) (2024)
- [Telephone Surveys Meet Conversational AI: Evaluating a LLM-Based Telephone Survey System at Scale](https://mlsanity.com/paper/2502.20140/) (2025)
- [A Model Is Not Built By A Single Prompt: LLM-Based Domain Modeling With Question Decomposition](https://mlsanity.com/paper/2410.09854/) (2024)

## Built on by (most cited first, 10 of 10)

- [PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models](https://mlsanity.com/paper/2601.10532/) (2026)
- [AI generates well-liked but templatic empathic responses](https://mlsanity.com/paper/2604.08479/) (2026)
- [Affective Context Amplifies Sycophancy in LLM Responses](https://mlsanity.com/paper/2608.21242/) (2026)
- [Stable Behavior, Limited Variation: Persona Validity in LLM Agents for Urban Sentiment Perception](https://mlsanity.com/paper/2604.28048/) (2026)
- [Persona Prompting in Multimodal Urban Perception: Descriptive Convergence and Interpretive Variation](https://mlsanity.com/paper/2605.29064/) (2026)
- [SPLIT: Cross-Lingual Empathy and Cultural Grounding in English and Ukrainian LLM Responses](https://mlsanity.com/paper/2607.02049/) (2026)
- [Evaluating the Hidden Costs of Personalization in Large Language Models](https://mlsanity.com/paper/2608.28833/) (2026)
- [Empathy Is Steerable but Multi-Axial: Mechanism Geometry and Persona Effects in LLMs](https://mlsanity.com/paper/2609.15654/) (2026)
- [CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems](https://mlsanity.com/paper/2609.22494/) (2026)
- [Extracting Persona Subspaces Through Iterative Nullspace Projection For Modulation](https://mlsanity.com/paper/2610.04676/) (2026)

## Abstract (arXiv)

Large Language Models' (LLMs) ability to converse naturally is empowered by their ability to empathetically understand and respond to their users. However, emotional experiences are shaped by demographic and cultural contexts. This raises an important question: Can LLMs demonstrate equitable empathy across diverse user groups? We propose a framework to investigate how LLMs' cognitive and affective empathy vary across user personas defined by intersecting demographic attributes. Our study introduces a novel intersectional analysis spanning 315 unique personas, constructed from combinations of age, culture, and gender, across four LLMs. Results show that attributes profoundly shape a model's empathetic responses. Interestingly, we see that adding multiple attributes at once can attenuate and reverse expected empathy patterns. We show that they broadly reflect real-world empathetic trends, with notable misalignments for certain groups, such as those from Confucian culture. We complement our quantitative findings with qualitative insights to uncover model behaviour patterns across different demographic groups. Our findings highlight the importance of designing empathy-aware LLMs that account for demographic diversity to promote more inclusive and equitable model behaviour.